BBA & NJT
\(+\) neurodebian, slicer, brainsfit, nipype, itk and more …
a pride: common way of doing things
… in a competitive world …
Registration \(=\) estimate an “optimal” geometric mapping between image pairs or image sets (e.g. Affine)
Similarity \(=\) a function relating one image to another, given a transformation (e.g. mutual information)
Diffeomorphisms \(=\) differentiable map with differentiable inverse (e.g. “silly putty”, viscous fluid)
Segmentation \(=\) labeling tissue or anatomy in images, usually automated (e.g. K-means)
Multivariate \(=\) using many voxels or measurements at once (e.g. PCA, \(p >> n\) ridge regression)
Multiple modality \(=\) using many modalities at once (e.g. DTI and T1 and BOLD)
MALF: multi-atlas label fusion - using anatomical dictionaries to label new data
Solutions to challenging statistical image processing problems usually need elements from each of the above
Francis Galton: Can we see criminality in the face?
(maybe he should have used ANTs?)
D’Arcy Thompson
… just do a better registration (tell story) …
References: @Horn1981, @Gee1993, @Grenander1993, @Thompson2001, @Miller2002, @Shen2002, @Arnold2014, @Thirion1998, @Rueckert1999, @Fischl2012, @Ashburner2012
plausible physical modeling of large, invertible deformations
“differentiable map with differentiable inverse”
… to correct a misconception about diffeomorphisms …
170,000+ lines of C++, 6\(+\) years of work, 15+ collaborators.
Generic mathematical methods that are tunable for application specific domains: no-free lunch
Deep testing on multiple platforms … osx, linux, windows.
Several “wins” in public knock-abouts ( Klein 2009, Murphy 2011, SATA 2012 and 2013, BRATS 2013, others )
An algorithm must use prior knowledge about a problem
to do well on that problem
Atropos segmentation, N4 inhomogeneity correction, Eigenanatomy, SCCAN, Prior-constrained PCA, and atlas-based label fusion and MALF (powerful expert systems for segmentation)
documentation is important
… developers can be blind to doc deficiencies
while users are blind to what we provide!
“One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure…. ART, SyN, IRTK, and SPM’s DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets.”
Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR, Neuroinformatics.
$ CreateDTICohort -h
COMMAND:
CreateDTICohort
OPTIONS:
-d, --image-dimensionality 2/3
-a, --dti-atlas inputDTIAtlasFileName
-x, --label-mask-image maskImageFileName
lowerThresholdFunction
-n, --noise-sigma <noiseSigma=18>
-p, --pathology label[<percentageChangeEig1=-0.05>,<percentageChangeAvgEig2andEig3=0.05>,<numberOfVoxels=all or percentageOfvoxels>]
-w, --dwi-parameters [B0Image,directionFile,bvalue]
[B0Image,schemeFile]
-r, --registered-population textFileWithFileNames.txt
-o, --output [outputDirectory,fileNameSeriesRootName,<numberOfControls=10>,<numberOfExperimentals=10>]
-h
--help